# Extreme Value Theorem - Find the distribution of $M_n$

This is my problem:

Consider an i.i.d sequence of random variables $$X_1, X_2,\ldots,X_n,$$ and define $$m_n = 1 - \min(X_1, \ldots, X_n)$$. Find the distribution of $$m_n$$ in terms of $$F_X$$ the CDF of X .

For context, I have solved a similar problem which is shown below:

Consider an i.i.d sequence of random variables $$X_1, X_2,\ldots, X_n,$$ and define $$M_n = \max(X_1, \ldots, X_n)$$.

Find the distribution of $$M_n$$ in terms of $$F_X$$ the CDF of X .

\begin{align} P(M_n \leq x) &= P(\max(X_1, \ldots, X_n) \leq x) \\&= P(X_1 \leq X_1,\ldots, X_n \leq x) \\&= P(X_1 \leq x)\ldots P(X_n \leq x) \\&= F_x(x) \ldots F_x(n) \\&=(F_x(x))^n \end{align}

I am just wondering if i am missing some rule between max() and min() that I could use to solve this problem? or could i use the following

$$P(\min(X,Y) \leq x) = F_x(x)+ F_y(x) - F_x(x) F_y(x)$$

, but I am not sure how to include that with the 1 - term in the probability function.

$$\{m_n\leq x\}=\{1-\min(X_1,\dots,X_n)\leq x\}=\{\min(X_1,\dots,X_n\geq1-x\}=$$$$\{X_1\geq 1-x\}\cap\dots\cap\{X_n\geq1-x\}$$
On base of independepence we find:$$P(\{X_1\geq 1-x\}\cap\dots\cap\{X_n\geq1-x\})=P(X\geq1-x)^n=(1-P(X<1-x))^n\tag1$$
Here: $$P(X<1-x)=\lim_{z\downarrow x}F_X(1-z)$$ and equals $$F_X(1-x)$$ if $$P(X=1-x)=0$$.
In that case you can write the RHS of $$(1)$$ as: $$(1-F_X(1-x))^n$$